Localization using signal receivers and or emitters, e.g. making use of an RF signal strength fingerprint for determining the localization of an object, is widely used. Typically, in order to obtain accurate results, an offline phase is performed whereby manual calibration of different points with a known location is done in order to calibrate the localization system. This calibration results in a fingerprint database storing the recorded signal strength for the points with known location. In a second phase, the database is used for comparison of a signal strength e.g. recorded by the object that is tracked with the fingerprint database.
Signal strengths are very dynamic and can be influenced for example by doors which are opened or closed, furniture that is moved, people that pass, external RF sources, environmental conditions, etc. As the actual fingerprint thus can differ with respect to the fingerprint stored in the database, this will result in inaccurate estimation of the localization of the pattern. A number of suggestions were provided for overcoming this inaccuracy.
In a first suggestion, recalibration of the whole environment is performed manually every time a modification in the RF environment has occurred. It has been found that this is feasible to cope with large alterations, e.g. when infrastructure has been changed, but not for small alterations, provided the change has been notified to the administrator of the system. A manual recalibration of the whole environment results in huge manual interaction and does not seem feasible to deal with short time fluctuations.
In another suggestion static reference measurement points are provided, allowing recalibration for example at predetermined time intervals. Using the static reference points, the signal strength is measured on different known locations and changes in the signal strength can be used to dynamically change the fingerprint database. Such a system only works provided that the static reference points are not altered in position. Furthermore, it requires the provision of static reference points which need to be powered. One example of such a recalibration system is described in U.S. Pat. No. 6,380,894, wherein geolocation errors associated with variations in parameters of the signal transport paths are effectively removed by installing one or more reference tags, whose geolocations are precisely known. Emission from the reference tags are processed and coupled to the geolocation processor and comparison allows adjusting the calibration based on these reference tags. Another example thereof is described in WO2009/072735, wherein an environment analysis tool is used and whereby explicit communication between the access point and a receiving terminal is provided.
In still another suggestion use is made of a plurality of additional sensors for measuring changing conditions such as for example when doors are opened or closed or if the humidity or temperature changes. This information can then be used for recalibrating the system. Such recalibration is limited in the number of aspects that can be taken into account for the change in fingerprint and requires providing and powering of sensors. Related thereto is recalibration by selection of a fingerprint selected from a set of fingerprints, e.g. recorded at different times during the day and thus taking into account varying signal path conditions during the day. An example of selection from a set of predefined fingerprints depending on the actual conditions is described in US2003/008668.
Recalibration using for example the above techniques may be initiated based on input of a user indicating the system that an estimated position is wrong. This requires user interaction in order to indicate to the system where the user is located.
There is still room for a more efficient recalibration of the localization system.